Running corrections for multiple comparisons in glmer

you could  "just" use p.adjust(), something like this:

library(lme4)
gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
                   data = cbpp, family = binomial)
cc <- coef(summary(gm1))
cc <- cbind(cc,adjust.p=p.adjust(cc[,"Pr(>|z|)"],"holm"))


  The general machinery in the multcomp package (especially the glht
function) should work.

This looks useful:

https://thebiobucket.blogspot.com/2011/06/glmm-with-custom-multiple-comparisons.html

  The bottom line is that most standard multiple-comparisons or
pairwise-comparisons machinery should "just work" with glmer fits.
(There are some open questions about what you're doing: it's a bit
unusual for people to apply multiple comparisons corrections on a set of
"only" 6 parameters specified a priori: Tukey adjustments to post hoc
pairwise comparisons are much more common ...)

Dear all,

A reviewer has asked me to apply a correction to multiple comparisons
conducted for a logistic mixed effect regression with binary outcome. The
model is:

glmer(outcome ~ factor1 * factor2 + (1|RE1) + (1|RE2), family =binomial,
data = data)

where factor 1 has two levels and factor 2 has three. Could you advise on
how to run this and how to report the adjusted p-values in the same table?
At the moment, my table has the following 6 headings:

Reference level

Contrasts

Estimate

SE

Wald *z*

*p*


Many thanks in advance,

Frank

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